%AI for:
Smart Home technology has been growing in the last years. New ideas and technology has come, old has been replaced and in the future this phenomenon will probably continue. The technologies can just as well be used in a corporate perspective, where the technologies can help creating better workplace environments.
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In this project the foundation for an agent-oriented programming approach to creating a Smart Office has been constructed and, in the virtual world of Jade, proved to work. The focus for the project has been to create a solid architecture that fairly easy can be expanded with more agents and thereby more types of messages.
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There are endless possibilities of things that can be integrated into a system of this kind (See \sectionref{analysis} Analysis). Not only can more sensors and actuators be added to the system. An obvious possibility would be to implement some artificial intelligence into the agents as well. Based on historical data collected the agents should learn when different things should be done. E.g. as mentioned in the Analysis the vacuum cleaners could learn that it is be best to do all of the cleaning at a certain time of the day. This time will typically be different depending on the office to clean because of the different people using them.
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From a learning perspective of the group members, working on this project have been interesting. Compared to the traditional object-oriented programming, does the oriented-oriented paradigm presents some benefits such as lower coupling between the agents compared to objects. Creating an ontology handling this low coupling has been one of the most interesting challenges of this project. 
It is a challenge to create something that is simple to use and so complex to and agile in containing information. It means, one method creates the message that could contain many different information and one method that retrieve the right message without know at hand, what kind of message it is.
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%\begin{itemize}
%  \item learning the best time for cleaning .
%  \item learning the dynamic preferences of the individuals regarding the season.
%\end{itemize}
